Much interest has been exhibited in the analysis or automatic extraction of informational content or intelligence from speech. Such automatic processing of recorded speech sounds generally employs the spectral analysis and correlation techniques in sophisticated electronic machines such as channel vocoders, automatic speech recognition, and automatic speaker recognition equipment. Such equipment subjects the spectral analyzers to various levels of performance degradation due to electrical background noise (so called "white" noise) and spectral variations in the signalling environment and signal channels. Such spectral variations may be referred to as transmission noise, comprised of "colored noise" and background tones (as distinguishing from so-called gaussian "white noise" or more practically speaking "box car" noise).
Various prior art attempts at signal reconstruction have dealt with different aspects of such multi-faceted signal distortion problem with only limited success. For example, attempts to compensatorily gain change a noisy signal (i.e., a combination of both a signal of interest plus noise) to offset spectral gain response variations in the transmission equipment, induces the gain control function to be performed upon the signal of interest as a function of the noise content while not removing such noise content. Also attempts to effect noise rejection by low-pass filtering merely rejects both noise and signal content outside a spectral region of interest while failing to suppress noise content within the spectral region of interest.
Thus, none of such prior art techniques are directed to combined compensation of a noisy speech signal for both noise content and spectral transmission variations.